In recent years, optical computing techniques have been developed for applications in the oil and gas industry in the form of optical sensors in downhole or surface equipment to evaluate a variety of fluid properties. In general, an optical computing device is a device configured to receive an input of electromagnetic radiation from a sample and produce an output of electromagnetic radiation from a processing element, also referred to as an optical element, wherein the output reflects the measured intensity of the electromagnetic radiation. The optical computing device may be, for example, an ICE. One type of an ICE is an optical thin film optical interference device, also known as a multivariate optical element (“MOE”).
Fundamentally, optical computing devices utilize optical elements to perform calculations, as opposed to the hardwired circuits of conventional electronic processors. When light from a light source interacts with a substance, unique physical and chemical information about the substance is encoded in the electromagnetic radiation that is reflected from, transmitted through, or radiated from the sample. Thus, the optical computing device, through use of the ICE and one or more detectors, is capable of extracting the information of one or multiple characteristics/analytes within a substance and converting that information into a detectable output signal reflecting the overall properties of a sample. Such characteristics may include, for example, the presence of certain elements, compositions, fluid phases, etc. existing within the substance.
Historically, ICEs have been designed using alternating layers of high index (e.g. Silicon) and low index (e.g. Silicon-di-oxide) materials on a transparent (e.g. BK7) substrate. The target ICE design is achieved by first generating a random design (random number of layers and layer thicknesses) and then running a minimization algorithm on the individual layer thicknesses using performance figure of merits such as the Standard Error in Calibration (SEC) or calibration sensitivity of the measurement as the error argument. The layer thicknesses, total number of layers, and the respective optical constants, of the high and low index materials and substrate, define the ICE design and thus its transmission profile. The transmission spectrum of ICE consists of transmission peaks and valleys across the range of wavelengths of interest. The transmission peaks/valleys are typically co-related to the analyte in question. The SEC and calibration sensitivity are calculated by projecting the transmission spectrum of the ICE design onto the optical database (i.e., the calibration data). The minimization algorithm stops when the lowest SEC or highest calibration sensitivity has been reached.
Certain ICE design algorithms are very advantageous in finding candidate designs having the lowest prediction error (SEC). However, it has been observed in recent studies that using an approach of beginning with randomized starting thickness may result in ½ of the possible maximum number of candidate designs with the highest calibration sensitivities. This approach requires more computing time and resources to achieve a larger number of candidate designs having the highest calibration sensitivities. Larger numbers of candidate designs are desired as they are further analyzed during the candidate finalization process, which involves spectrum visualization, parameter cross-plotting and tolerance-based fabrication analysis. Many candidate designs are rejected during this process. As a result, the fewer candidate designs with high calibration sensitivity enter the candidate finalization process, the fewer designs are output to choose from.
In view of the foregoing, there is a need in the art for an efficient, cost-effective ICE design technique to increase available candidate designs with high calibration sensitivity. Such a design technique would reduce computing requirements and computation cost.